Bibliographic record
Abstract
The medical education community is reflecting increasingly on the role and nature of research in the field. Useful sources of data to include in these reflections are a description of the topics in which we are investing our energies, an analysis of the extent to which there is a sense of progress on these topics, and an examination of the mechanisms by which any progress has been achieved. This article presents the results of a thematic review of the medical education research literature in four key journals since the turn of the 21st century. It describes four examples of areas in which the community appears to be investing its energies: curriculum and teaching issues, skills and attitudes relevant to the structure of the profession, individual characteristics of medical students, and the evaluation of students and residents. A discussion of the recent publications in these domains highlights a distinction between thematic categories of research, in which many members of the community are working on the same topic, and programmatic lines of research, in which members of the community are working together toward the shared goal of consensual understanding. The author suggests that community-level, programmatic lines of research are necessary to build knowledge and understanding of a domain and that, in the absence of such communal effort, the value of research is limited to the uncoordinated accrual of information.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.016 | 0.028 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".